613 citations · 849 across the 26 of their papers we have counts for
6 papers · 1 filter
SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder
Hyojin Park, Lars Lowe Sjösund, YoungJoon Yoo +3
Designing a lightweight and robust portrait segmentation algorithm is an important task for a wide range of face applications. However, the problem has been considered as a subset…
Neural Approximation of an Auto-Regressive Process through Confidence Guided Sampling
YoungJoon Yoo, Sanghyuk Chun, Sangdoo Yun +2
We propose a generic confidence-based approximation that can be plugged in and simplify the auto-regressive generation process with a proved convergence. We first assume that the p…
Variational Autoencoded Regression: High Dimensional Regression of Visual Data on Complex Manifold
YoungJoon Yoo, Sangdoo Yun, Hyung Jin Chang +2
This paper proposes a new high dimensional regression method by merging Gaussian process regression into a variational autoencoder framework. In contrast to other regression method…
ExtremeC3Net: Extreme Lightweight Portrait Segmentation Networks using Advanced C3-modules
Hyojin Park, Lars Lowe Sjösund, YoungJoon Yoo +2
Designing a lightweight and robust portrait segmentation algorithm is an important task for a wide range of face applications. However, the problem has been considered as a subset…
EXTD: Extremely Tiny Face Detector via Iterative Filter Reuse
YoungJoon Yoo, Dongyoon Han, Sangdoo Yun
In this paper, we propose a new multi-scale face detector having an extremely tiny number of parameters (EXTD),less than 0.1 million, as well as achieving comparable performance to…
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh +3
Regional dropout strategies have been proposed to enhance the performance of convolutional neural network classifiers. They have proved to be effective for guiding the model to att…